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import streamlit as st | |
import os | |
import pathlib | |
import textwrap | |
from PIL import Image | |
import google.generativeai as genai | |
genai.configure(api_key='AIzaSyAA_R5VXv1qjJ5jDMObkluREA8BxJO67RU') | |
## Function to load OpenAI model and get respones | |
def get_gemini_response(input,image,prompt): | |
model = genai.GenerativeModel('gemini-pro-vision') | |
response = model.generate_content([input,image[0],prompt]) | |
return response.text | |
def input_image_setup(uploaded_file): | |
# Check if a file has been uploaded | |
if uploaded_file is not None: | |
# Read the file into bytes | |
bytes_data = uploaded_file.getvalue() | |
image_parts = [ | |
{ | |
"mime_type": uploaded_file.type, # Get the mime type of the uploaded file | |
"data": bytes_data | |
} | |
] | |
return image_parts | |
else: | |
raise FileNotFoundError("No file uploaded") | |
##initialize our streamlit app | |
st.set_page_config(page_title="Gemini Image Demo") | |
st.header("Generative AI : Invoice Reader") | |
input=st.text_input("Input Prompt: ",key="input") | |
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
image="" | |
if uploaded_file is not None: | |
image = Image.open(uploaded_file) | |
st.image(image, caption="Uploaded Image.", use_column_width=True) | |
submit=st.button("Tell me about the image") | |
input_prompt = """ | |
You are an expert in pharmaceutical field, reading medicine names. | |
You will receive input images as prescription & | |
you will have to detect medicine names as correctly | |
from complicated and poor english handwriting, as done by an OCR model | |
""" | |
## If ask button is clicked | |
if submit: | |
image_data = input_image_setup(uploaded_file) | |
response=get_gemini_response(input_prompt,image_data,input) | |
st.subheader("The Response is") | |
st.write(response) |